AIMC Topic: Cohort Studies
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Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.
Frontiers in endocrinology
Jun 10, 2024
OBJECTIVE: The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtr...
Artificial Intelligence vs. Doctors: Diagnosing Necrotizing Enterocolitis on Abdominal Radiographs.
Journal of pediatric surgery
Jun 8, 2024
BACKGROUND: Radiographic diagnosis of necrotizing enterocolitis (NEC) is challenging. Deep learning models may improve accuracy by recognizing subtle imaging patterns. We hypothesized it would perform with comparable accuracy to that of senior surgic...
Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study.
Biological psychiatry
Jun 7, 2024
BACKGROUND: Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for chan...
Development and external validation of a machine learning model for the prediction of persistent acute kidney injury stage 3 in multi-centric, multi-national intensive care cohorts.
Critical care (London, England)
Jun 4, 2024
BACKGROUND: The aim of this retrospective cohort study was to develop and validate on multiple international datasets a real-time machine learning model able to accurately predict persistent acute kidney injury (AKI) in the intensive care unit (ICU).
Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study.
BMC neurology
May 27, 2024
BACKGROUND: Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the e...
Modeling mortality prediction in older adults with dementia receiving COVID-19 vaccination.
BMC geriatrics
May 24, 2024
OBJECTIVE: This study compared COVID-19 outcomes between vaccinated and unvaccinated older adults with and without cognitive impairment.
A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort.
Psychiatry research
May 23, 2024
Perinatal depression (PND) is a common complication of pregnancy associated with serious health consequences for both mothers and their babies. Identifying risk factors for PND is key to early detect women at increased risk of developing this conditi...
Prediction of Cognitive Impairment Risk among Older Adults: A Machine Learning-Based Comparative Study and Model Development.
Dementia and geriatric cognitive disorders
May 22, 2024
INTRODUCTION: The prevalence of cognitive impairment and dementia in the older population is increasing, and thereby, early detection of cognitive decline is essential for effective intervention.
Intraoperative left-sided colorectal anastomotic testing in clinical practice: a multi-treatment machine-learning analysis of the iCral3 prospective cohort.
Updates in surgery
May 20, 2024
BACKGROUND: Current evidence about intraoperative anastomotic testing after left-sided colorectal resections is still controversial. The aim of this study was to analyze the impact of Indocyanine Green fluorescent angiography (ICG-FA) and air-leak te...
Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.
BMC palliative care
May 20, 2024
BACKGROUND: Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However, gaps in ...
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